Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM

  • Unique Paper ID: 180483
  • Volume: 12
  • Issue: 1
  • PageNo: 1337-1341
  • Abstract:
  • Respiratory diseases rank among the foremost causes of mortality globally. While traditional lung auscultation is effective, it is hindered by limitations such as interference from background noise and reliance on the expertise of healthcare professionals. Recently, machine learning has emerged as a promising approach for the automated analysis of lung sounds, enhancing diagnostic accuracy and reducing the time required for diagnosis. This study is dedicated to the development of an automated system for lung sound classification, utilizing GTCC-based features in conjunction with a Multi-Layer Perceptron (MLP) classifier. Our system, trained on a comprehensive dataset comprising over 6,800 audio clips, achieved an impressive classification accuracy of 99.22%, underscoring its potential to facilitate the early detection of respiratory diseases.

Copyright & License

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

BibTeX

@article{180483,
        author = {Prof. Shah Saloni Niranjan and Mr. Pawar R.B. and Ms. Raut A.S. and Ms. Jadhav S.N.},
        title = {Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {1337-1341},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180483},
        abstract = {Respiratory diseases rank among the 
foremost causes of mortality globally. While traditional 
lung auscultation is effective, it is hindered by 
limitations such as interference from background noise 
and reliance on the expertise of healthcare 
professionals. Recently, machine learning has emerged 
as a promising approach for the automated analysis of 
lung sounds, enhancing diagnostic accuracy and 
reducing the time required for diagnosis. This study is 
dedicated to the development of an automated system 
for lung sound classification, utilizing GTCC-based 
features in conjunction with a Multi-Layer Perceptron 
(MLP) classifier. Our system, trained on a 
comprehensive dataset comprising over 6,800 audio 
clips, achieved an impressive classification accuracy of 
99.22%, underscoring its potential to facilitate the 
early detection of respiratory diseases.},
        keywords = {Machine Learning, Lung Sound Analysis,  GTCC Features, Deep Learning, Respiratory Diseases.},
        month = {June},
        }

Cite This Article

  • ISSN: 2349-6002
  • Volume: 12
  • Issue: 1
  • PageNo: 1337-1341

Diagnosing Respiratory Conditions Via Lung Sounds using CNN-LSTM

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